Predicting atrial fibrillation after cardiac surgery using a simplified risk index

Hung, Le Thanh; Alshareef, Abdulmueti; Al-Ahdal, Tareq; Anh, Pham Tho Tuan; Huan, Do Quang; Trang, Do Van; Zia, Sairah; Sy, Hoang Van; Huy, Nguyen Tien

doi:10.1016/j.jelectrocard.2021.03.012

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…In our study, the increase in the left atrium diameter was associated with the risk of AF development [10]. The obtained data coincide with the results of Hung LT et al (2021) [11] but differ from the opinion of Jakubova M. et al (2012) [12]. According to the literature data, the involvement of the left atrium can be explained by an increase in fibrosis development with subsequent atrial dilatation.…”

Section: Discussionsupporting

confidence: 76%

Comprehensive Analysis of Factors Associated with New Episode of Postoperative Atrial Fibrillation after Coronary Artery Bypass Graft Surgery

Rubanenko,

Davydkin

2023

Life

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The aim of the study was to perform a comprehensive fundamental analysis of the factors of inflammation, oxidative stress, fibrosis, myocardial dysfunction, ischemia and omega-3 index associated with postoperative atrial fibrillation (POAF) after coronary artery bypass graft (CABG) surgery in patients with coronary artery disease. The study involved 158 patients who were admitted to the hospital to undergo CABG surgery. Patients were divided into two groups: group 1 comprised 111 patients without POAF (82% males, median age—62.0 (56.0; 66.0) years), and group 2 comprised 47 patients with POAF (84.4% males, median age—65.0 (61.0; 70.0) years). POAF occurred 5.2 (2.0; 7.0) days after CABG. In all the patients, we evaluated laboratory tests before and 3–4 days after CABG. All the patients also underwent echocardiography. According to results of multifactorial regression analysis, the odds ratio of POAF development for left atrial diameter >41 mm was 4.3 (95% confidence interval (CI) 2.0–9.7, p < 0.001), interleukin (IL)-6 postoperative levels >22.07 pg/mL—3.0 (95% CI 1.4–8.2, p = 0.006), IL-8 postoperative levels >9.67 pg/mL—2.3 (95% CI 1.2–7.3, p = 0.006), superoxide dismutase postoperative levels in plasma >1100.5 U/g—3.2 (95% CI 1.4–9.2, p = 0.03), glutathione postoperative levels ≤0.194 micromole/g of hemoglobin—1.9 (95% CI 1.2–6.3, p < 0.001), glutathione peroxidase postoperative levels ≤17.36 millimole/g of hemoglobin—2.2 (95% CI 1.1–8.2, p < 0.001), glutathione reductase postoperative levels ≤2.99 millimole/g of hemoglobin—2.3 (95% CI, 1.1–5.7, p < 0.001), malondialdehyde postoperative levels >1.25 micromole/g of hemoglobin—2.0 (95% CI, 1.2–7.9, p < 0.001), NO postoperative levels in plasma >36.4 micromole/L—1.5 (95% CI, 1.1–5.9, p < 0.001) and omega-3 index ≤1.59%—2.6 (95% CI 1.5–9.1, p < 0.001). Our study showed that increased left atrial diameter, high postoperative levels of inflammatory factors, oxidative stress, fibrosis indicators and omega-3 index were associated with POAF in patients who underwent CABG.

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Section: Discussionsupporting

confidence: 76%

Comprehensive Analysis of Factors Associated with New Episode of Postoperative Atrial Fibrillation after Coronary Artery Bypass Graft Surgery

Rubanenko,

Davydkin

2023

Life

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“…Te results showed that the CABG risk prediction system overestimated its mortality rate for subgroup. Hung et al [31] have developed and verifed a simple risk score based on clinical variables, which can accurately predict the risk of complications in patients undergoing cardiac surgery before surgery and have a similar predictive function as this study.…”

Section: Grouped Based On Cabg Risk Prediction Systemmentioning

confidence: 80%

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting

Tang

Wang

Shi

et al. 2023

Computational Intelligence and Neuroscience

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The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87–2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82–0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.

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Risk Scores for Prediction of Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Meta-Analysis

Pandey,

Okaj,

Ichhpuniani

et al. 2023

The American Journal of Cardiology

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Predicting atrial fibrillation after cardiac surgery using a simplified risk index

Cited by 5 publications

References 28 publications

Comprehensive Analysis of Factors Associated with New Episode of Postoperative Atrial Fibrillation after Coronary Artery Bypass Graft Surgery

Comprehensive Analysis of Factors Associated with New Episode of Postoperative Atrial Fibrillation after Coronary Artery Bypass Graft Surgery

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting

Risk Scores for Prediction of Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Meta-Analysis

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